{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history saving thread hit an unexpected error (OperationalError('database or disk is full',)).History will not be written to the database.\n"
     ]
    }
   ],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your Answer**: *fill this in.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question 2**\n",
    "We can also other distance metrics such as L1 distance.\n",
    "The performance of a Nearest Neighbor classifier that uses L1 distance will not change if (Select all that apply.):\n",
    "1. The data is preprocessed by subtracting the mean.\n",
    "2. The data is preprocessed by subtracting the mean and dividing by the standard deviation.\n",
    "3. The coordinate axes for the data are rotated.\n",
    "4. None of the above.\n",
    "\n",
    "*Your Answer*:\n",
    "\n",
    "*Your explanation*:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 49.184092 seconds\n",
      "One loop version took 57.022511 seconds\n",
      "No loop version took 1.571616 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# you should see significantly faster performance with the fully vectorized implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.254000\n",
      "k = 1, accuracy = 0.198000\n",
      "k = 1, accuracy = 0.228000\n",
      "k = 1, accuracy = 0.244000\n",
      "k = 1, accuracy = 0.216000\n",
      "k = 3, accuracy = 0.230000\n",
      "k = 3, accuracy = 0.210000\n",
      "k = 3, accuracy = 0.228000\n",
      "k = 3, accuracy = 0.226000\n",
      "k = 3, accuracy = 0.214000\n",
      "k = 5, accuracy = 0.218000\n",
      "k = 5, accuracy = 0.228000\n",
      "k = 5, accuracy = 0.210000\n",
      "k = 5, accuracy = 0.230000\n",
      "k = 5, accuracy = 0.222000\n",
      "k = 8, accuracy = 0.206000\n",
      "k = 8, accuracy = 0.248000\n",
      "k = 8, accuracy = 0.224000\n",
      "k = 8, accuracy = 0.194000\n",
      "k = 8, accuracy = 0.252000\n",
      "k = 10, accuracy = 0.202000\n",
      "k = 10, accuracy = 0.240000\n",
      "k = 10, accuracy = 0.216000\n",
      "k = 10, accuracy = 0.216000\n",
      "k = 10, accuracy = 0.232000\n",
      "k = 12, accuracy = 0.190000\n",
      "k = 12, accuracy = 0.238000\n",
      "k = 12, accuracy = 0.222000\n",
      "k = 12, accuracy = 0.212000\n",
      "k = 12, accuracy = 0.238000\n",
      "k = 15, accuracy = 0.202000\n",
      "k = 15, accuracy = 0.230000\n",
      "k = 15, accuracy = 0.210000\n",
      "k = 15, accuracy = 0.202000\n",
      "k = 15, accuracy = 0.228000\n",
      "k = 20, accuracy = 0.206000\n",
      "k = 20, accuracy = 0.212000\n",
      "k = 20, accuracy = 0.218000\n",
      "k = 20, accuracy = 0.212000\n",
      "k = 20, accuracy = 0.214000\n",
      "k = 50, accuracy = 0.206000\n",
      "k = 50, accuracy = 0.204000\n",
      "k = 50, accuracy = 0.202000\n",
      "k = 50, accuracy = 0.210000\n",
      "k = 50, accuracy = 0.228000\n",
      "k = 100, accuracy = 0.200000\n",
      "k = 100, accuracy = 0.206000\n",
      "k = 100, accuracy = 0.192000\n",
      "k = 100, accuracy = 0.208000\n",
      "k = 100, accuracy = 0.234000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "X_train_folds = np.array_split(X_train, num_folds)\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "for k in k_choices:\n",
    "    k_to_accuracies[k] = []\n",
    "    for f in range(num_folds):\n",
    "        classifier.train(X_train_folds[f], y_train_folds[f])\n",
    "        tmp_dists = classifier.compute_distances_no_loops(X_test)\n",
    "        y_test_pred = classifier.predict_labels(tmp_dists, k)\n",
    "        num_correct = np.sum(y_test_pred == y_test)\n",
    "        accuracy = float(num_correct) / num_test\n",
    "        k_to_accuracies[k].append(accuracy)\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 1\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question 3**\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The training error of a 1-NN will always be better than that of 5-NN.\n",
    "2. The test error of a 1-NN will always be better than that of a 5-NN.\n",
    "3. The decision boundary of the k-NN classifier is linear.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "*Your Answer*:\n",
    "\n",
    "*Your explanation*:"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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